supervised learning

Discover the Power of Supervised Learning

Supervised learning is a type of machine learning, where we train a model on labeled data.

But what does any of that even mean?

Well, as it’s name suggests, there is some kind of supervision involved. Furthermore, we supervise the training process of the model by passing to our model a training example data on the input and output.

The input data will simply be data from which a model will determine what its output should be. Therefore, when a model is training on a dataset, it will correct its variables, so its output matches that which we expect.

In other words, it finds patterns from input data and maps them to the output data, which we call labels.

One of the main advantages of supervised learning is that it can predict very accurate results. However, the accuracy does depend on the quality of the training dataset and how well we tune our model.

Types of Supervised Learning

There are 2 main types of supervised learning, classification and regression. Classification sorts data between several classes or categories, while regression predicts a continuous value.


For example, we have a dataset of images with cats and dogs and our goal is to sort these images between the 2. Therefore, we pass an image as the input data and output 2 different values, each representing a class.

Furthermore, after we train our model, it should output values between 0 and 1 for each class. And if we train it so it gives reliable results, it will output a clear difference, indicating a high certainty level that its answer is correct.

There are also different types of classification algorithms, such as decision trees, k-nearest neighbors and support vector machines.


As I mentioned before, regression is a type of supervised learning, where a model is trained to predict a continuous value. For example, we could train a model to predict house prices based on their locations, size, year built…

Some examples of regression algorithms include linear regression and polynomial regression. Linear regression is one of the simplest ones there is, since it predicts values on a straight line.


In conclusion, supervised learning is a powerful tool for making accurate predictions. However, it does require a significant amount of labeled data and can make predictions only in the scope of that data.

I hope this article helped you gain a better understanding about supervised learning. And perhaps even inspire you to learn more about machine learning.

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